This invention relates generally to the field of seismic interpretation. Specifically, the invention is a method for performing a deterministic analysis of the likelihood of connectivity of seismic objects that satisfy certain attribute criteria.
A common problem in 3D seismic interpretation is the extraction of geologic features from an attribute volume and evaluation of their geometric relationships to each other and implications for connectivity. Bulk processing of a seismic volume results in the detection of one or more seismic xe2x80x9cobjectsxe2x80x9d at a certain attribute threshold. An xe2x80x9cobjectxe2x80x9d is defined as a region in which the value of a certain selected seismic attribute (acoustic impedance, for example) satisfies some arbitrary threshold requirement, i.e. is either greater than some minimum value or is less than some maximum value. At a certain threshold, two such regions may not be connected (i.e., they are two objects); at a different threshold, they may be connected (i.e., a single object). The interpreter must decide which threshold depicts a scenario that is more consistent with other known information about the area. Selection of an appropriate threshold is not always straightforward and it may take multiple iterations to achieve the desired result. [The desired result of course, is that the seismic objects should correspond to actual underground reservoirs, and where two objects are interpreted as either connected or not, that would mean that the corresponding reservoirs if they contain oil or gas, would also be connected (or not), meaning that a well producing one reservoir can also drain the other (or cannot).] Interpretation time could be reduced significantly if one could bulk process a seismic volume, and generate a collection of seismic objects detected at various attribute thresholds as well as information about connectivity of these objects.
Identification of seismic objects using various seismic attributes as indicators is known in the seismic art, as partially summarized below. All such known methods are deficient in providing sufficient information about how the objects thus identified might be connected or further subdivide at different attribute and object size thresholds.
In the following paragraphs these terms will be used more or less interchangeably:
cell, voxel, point
geobody, seismic object, object
The technique commonly known as seed picking results in a set of voxels in a 3D volume, which fulfil user-specified attribute criteria and are connected. The technique has been implemented in several commercial products such as VoxelGeo, GeoViz, Gocad, Jason and others. It is an interactive method, where the user specifies the initial xe2x80x9cseedxe2x80x9d point and attribute criteria. The algorithm marks the initial point as belonging to the current object, and tries to find this point""s neighbors that satisfy the specified attribute criteria. The new points are added to the current object, and the procedure continues until it is not possible to find any new neighbors fulfilling the specified criteria.
Seed picking requires a criterion for connectedness. There are two criteria commonly used, although others may be defined and used. One definition is that two cells are connected (i.e., are neighbors) if they share a common face. By this definition of connectivity, a cell (or voxel) can have up to six neighbors. The other common criterion for being a neighbor is sharing either an edge, a face, or a corner. By this criterion, a cell can have up to twenty-six neighbors.
There are a number of examples in the literature, which describe detection of geobodies or seismic objects based on seed picking.
Seed picking may have originated in medical applications. For example, 1988 U.S. Pat. No. 4,751,643 to William Lorensen and Harvey Cline discloses a specific seed picking algorithm that enables radiologists and surgeons to display only bone tissue or only soft tissue and provides them with extensive preoperative information. The algorithm is claimed to be very fast because it accesses the original data values only once. The first step is labeling, which means checking the attribute criteria for each voxel. It marks cells fulfilling the criteria as 1, and the others as 0. Then the connectivity (region growing) algorithm is employed which works on this single-bit data set.
In the oil and gas industry, object identification by seed picking has become widespread although few papers describe specific seed picking algorithms, probably because the use of seed picking algorithms implemented in commercial software products like VoxelGeo are so available.
The method disclosed in U.S. Pat. No. 5,586,082 to Roger Anderson, et al. is an example of a seed growing method of detecting seismic objects with an interest in how these objects, distinct at one threshold of the chosen attribute, may be connected at another threshold. Anderson""s method identifies high amplitude regions, suggestive of petroleum presence using seismic attribute analysis, with the object of determining oil or gas migration pathways connecting those regions, or alternatively to determine that certain regions are unconnected. Anderson""s method depends on having and analyzing multiple 3-D seismic surveys of the same region acquired at different times, using the small changes to suggest the drainage pathways and connectivity.
What is needed to improve upon existing seed detection methods is a fast method of determining from a single seismic data set the connectivity between objects that may be connected at certain attribute thresholds but not at others, and a systematic way of keeping track of such connectivity as a function of attribute threshold. The present inventive method satisfies these needs.
In one embodiment the present invention is a method for predicting connectivity of seismic objects determined from seismic data collected from a subterranean region, where the method comprises the steps of (a) dividing the subterranean region into cells and determining from the seismic data the value of a preselected seismic attribute in each cell; (b) choosing a threshold criterion for the value of the seismic attribute; (c) determining for each cell whether the value of the selected attribute for that cell satisfies the chosen criterion; (d) identifying seismic objects containing only connected cells that satisfy the attribute criterion, using a pre-selected definition of connectivity; (e) repeating steps (b)-(d) for at least one different value of the attribute threshold; and (f) tracking each seismic object identified for changes in its size, spatial position, and connection to other objects, all as a function of attribute threshold value.
In some embodiments of the invention, objects are discarded if they are smaller in size than a pre-selected minimum size. In other embodiments, objects are discarded if they are larger than a preselected maximum size. In some preferred embodiments, the attribute and/or object size thresholds are varied beginning with the least restrictive values and progressing to the most restrictive values.
In some preferred embodiments of the invention, a 3-D visual display is used to present the results in step (d) above, thereby aiding the tracking of step (f). In some preferred embodiments, a 2-D hierarchical tree is used to graphically display the findings of step (f).
The results of step (f) may be used to predict connectivity of actual hydrocarbon-bearing formations.